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The authors have diligently addressed the suggested revisions, have significantly improved the quality of the manuscript and I recommend it for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
I appreciate author's effort to address all the reviewer's comment adequately. However, one of the reviewers suggests to do one final proof reading to avoid grammatical errors.
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The authors have addressed most of my comments. In particular, the validation results presented using GSE61144 would have increased the value of this study. While I would like to convey two additional comments, I think this revised manuscript is conducive to approval.
1. Dataset GSE61144 is part of GSE61145 and there is another dataset GSE60993 that includes 7 normal and 7 STEMI (AMI) samples. Therefore, a validation analysis using GSE60993 would be interesting. However, since the analyses in this manuscript were performed using datasets provided by different laboratories, GSE61144 and GSE97320, the revised manuscript seems to be an appropriate response to the review comments.
2. The responses to points 2 and 3 were the same answer. I had expected that the response to point 3 would be addressed. However, since it is an additional analysis from the manuscript as a whole, the logical structure is expected to be maintained even if it is omitted.
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The authors have addressed my concerns and I have no technical issues with this article. However, some minor proof reading is still needed.
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The manuscript has been assessed by three independent reviewers and I strongly suggest addressing the concerns raised by all three reviewers before your paper could be considered for publication.
1. Grammatical and typo errors needs to be fixed.
2. Language revision is highly recommended for better understanding of the manuscript.
3. The authors are suggested to cite previous studies and justify the novelty and significance of the current study.
4. Figures need to be improved in terms of labeling and description. Authors are also recommended to maintain uniformity in data representation.
5. Result section needs to be elaborated and discussed clearly.
6. Authors are suggested to provide more validation for the use of NRDEGs as prognostic markers in AMI patients.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The Academic Editor has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at copyediting@peerj.com for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
1. Please conduct a more comprehensive literature review on AMI and immune cell infiltration. For example, there are several papers published recently on this topic, including (but not limited to):
Zheng, PF., Zou, QC., Chen, LZ. et al. Identifying patterns of immune related cells and genes in the peripheral blood of acute myocardial infarction patients using a small cohort. J Transl Med 20, 321 (2022). https://doi.org/10.1186/s12967-022-03517-1
Xie Y, Wang Y, Zhao L, Wang F, Fang J. Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy. Bioengineered. 2021;12(1):2890-2905. doi:10.1080/21655979.2021.1937906
Wu Y, Jiang T, Hua J, et al. Integrated Bioinformatics-Based Analysis of Hub Genes and the Mechanism of Immune Infiltration Associated With Acute Myocardial Infarction. Front Cardiovasc Med. 2022;9:831605. Published 2022 Apr 6. doi:10.3389/fcvm.2022.831605
2. Please include the full citations for software/tools: DAVID, KEGG, CIBERSORT, R packages, etc. You could find the citations on their websites.
3. The English language could be improved to ensure that the audience can clearly understand your text. Some examples could be improved include lines 75 (Aiming to NRDEGs, …. Please clarify what use full name of NRDEG in its first appearance and explain how to obtain NRDEGs), line 89 (… whereas heat map R package..), line 124 (just data…), line 104 (was applied [to extract PPI pairs]), etc.
1. Please provide a github or a link to the codebase so that the readers can reproduce the results.
2. The previous article published by Wu et al. (doi:10.3389/fcvm.2022.831605) had applied (almost) same analysis pipeline and also used GSE66360 dataset. Please compare the results with their findings (e.g. similarity and differences). It'd be great to add some additional analyses or use other software tools to visualize and demonstrate your findings.
Section 3.1:
1. It’s not clear about line 144 and Figure 1A. Normalization is a preprocessing step before the differential analysis (not through DE analysis). Please clarify the rationale of including Figure1A or provide more details about the difference between normalization mentioned in line 144 and in 146.
2. Figure1: keep the same color coding for normal and AMI (yellow/blue) for the heat map side bar (B and E) and volcano plot (C).
3. Figure 1B and 1C: please add the legend for color in the heatmap (e.g. scaled of normalized value?).
4. Please provide a more detailed figure legend (including the explanation for color coding) and include more details in the results section. For example, it’s not clear how genes were selected in Figure1B.
5. Given that the normalized data are comparable, it’s possible that the differential results are driven by several outliers. Please use a more stringent cutoff for DEG (e.g. |log2FC| > 1.5) and discuss the results accordingly. From Figure 1C, it seems that most of the DEGs (colored in green and yellow) are having small fold change.
6. Please discuss the heatmaps (Figure 1B and E) in details. For example, what trend would you observe? is there any specific gene that worth highlighting?
Section 3.2:
1. Line 153: what’s is the online approach? Using the software name and include citation will be sufficient.
2. Please describe briefly about how adjusted p-value is performed in KEGG and how does it differ from FDR. Otherwise, it might be misleading to people who are not familiar with KEGG.
3. Please include an analysis using a more stringent cut off for FDR/q-value.
Section 3.4:
1. Please use k-fold cross validation in the ROC curve analysis (e.g. K=10) and for each panel, include a mean ROC curve with confidence interval.
Section 3.5:
1. Please include FDR/adjusted p-value to account for multiple testing across all examined cell types. Discuss the cell types with FDR/adjusted p-value < 0.1.
2. External validation dataset: please provide a description for GSE97320, including sample size for each group. Also, please try to search for a larger cohort to conduct the validation - for example, using some datasets from Wu et al. (doi:10.3389/fcvm.2022.831605).
3. Please use ROC curve with k-fold/leave-one-out cross validation.
In this manuscript, the authors investigated the expression profiles of necroptosis-related genes in acute myocardial infarction (AMI) and found six hub genes and their diagnostic value. The research objective is valid and the results are clear and validated by another dataset and their own wet experiments. Therefore, their findings seem valuable. However, there are several points need to be improved:
1. This manuscript needs a thorough review of previous research. There seems to be some related studies. e.g.) A review related with necroptosis and cardiovascular disease, including AMI [1]. A meta-analysis of gene expression profiles in myocardial infarction [2]. A gene expression analysis of apoptosis/ferroptosis/necroptosis/pyroptosis and AMI [3]. In this context, molecular mechanisms of necroptosis in AMI seems to be an area of interest rather than novelty.
[1] https://pubmed.ncbi.nlm.nih.gov/30034339/
[2] https://pubmed.ncbi.nlm.nih.gov/30482209/
[3] https://pubmed.ncbi.nlm.nih.gov/35432334/
2. As it is still a preprint, it is not related to this peer review process, but very similar work has been done using GSE66360 [4].
[4] https://www.researchsquare.com/article/rs-3002655/v1
3. In addition to Figure 2, it is desirable to perform GEO and KEGG enrichment analysis of GSE66360 DEG (1676+15) and necroptosis gene (15+144).
4. “TRAF5 was down-regulated in AMI samples, suggesting that it potentially suppressed necroptosis in AMI.” (l. 248-249, Figure 4D, Figure 7J). Does this lead to inconsistent results? A comprehensive interpretation should be given for the 6 hub NRDEGs.
5. Interpretation of immune infiltration analysis should include not only the statistical significance, but also the up-down directionality in Figure 5. Such as explaining the implications of upregulated neutrophils in the AMI group.
6. GSE97320 has only 6 samples, 3 AMIs and 3 good samples. Dot plots are better than violin plots for small samples. Furthermore, Figure 7I shows a similar distribution between the normal and AMI groups, although the p-value for TNPAIP3 is < 0.001. Statistics and distributions seem inconsistent.
7. Does NRDEGs mean necroptosis-related differentially expressed genes? There is no definition for the abbreviation NRDEGs.
8. Typo? NRFEGs (l. 38), NEDEGs (l. 271)
9. l. 175. Figure 4A --> Figure 4A, 4B?
10. l. 177. Figure 5B --> Figure 4C, 4D?
In this study the authors have first utilized and analyzed the GSE66360 data base to identify 15 unique differentially expressed necroptosis-related genes (NRDEGs) in patients suffering from Acute myocardial infraction (AMI). Multiple enrichment terms associated with necroptosis were discovered through GO together with KEGG analysis. They further filtered out a list of 10 hub NRDEGs and 6 hub NRDEGs toped the pareto based on ROC analysis (threshold of AUC > 0.7) performed by the authors. These 6 hub NRDEGs were - TNF, IL1B, TNFAIP3, TRAF5, NLRP3 and TLR4. And the ROC analysis did suggest that these were highly sensitive and specific for AMI patient samples. Further the gene expression analysis from the GSE66360 set indicated that these 6 hub NRDEGs were upregulated in AMI patient samples compared to normal samples. The authors have also explored the relationship of these NRDEG with immune cell infiltration potentially pointing out molecular mechanisms regulating AMI. The correlation analysis between the 6 hub NRDEGs and immune infiltrating cells provides some valuable insights into the potential mechanism of necroptosis in AMI patients. Finally, the authors validated the diagnostic power of the 6 identified NRDEGs against GSE97320 dataset. 4 out of the 6 NRDEG (TNF, TRAF5, NLRP3 and TLR4) had AUC > 0.7 in the independent data set, while IL1B and TNFAIP3 did not meet this threshold in this new data set. This could imply that TNF, TRAF5, NLRP3 and TLR4 might be the strongest biomarkers that could help in early diagnosis of AMI. RT-qPCR further validated their findings.
Major Suggestions/Edits:
1. In the abstract the authors have defined Necroptosis-related genes - NRGEG but have then used NRDEG and NRFEG without any terminology explanation. Either these are typos or need to be spelt out
2. In the introduction, the need and the impact of this study is not highlighted. There is a need to expand the introduction to better explain the problem statement at hand and emphasize the significance of this study. These aspects are a major weak point of this article.
3. Section 2.6 for the first and 2nd paragraph the first sentences are identical and redundant. Plz fix this.
4. Section 3.4, line 175 should refer to fig 4A and 4B.
5. Sec 3.4 line 177 should be referring to Fig 4C and 4D and not Fig 5B. Plz correct this as fig 5B is immune cell composition fraction indicating immune cell infiltration between normal vs AMI patient samples in GSE66360 data set.
6. Sec 3.5 Immune cell infiltration, in this section the authors have stated ”13 immune cell types exhibited obvious differences between two groups (p < 0.05), including Dendritic cells activated, Dendritic cells resting, Mast cells activated, Mast cells resting, Monocytes, Neutrophils, NK cells activated, NK cells resting, T cells CD4 memory resting, T cells follicular helper, T cells CD8, T cells regulatory (Tregs), together with T cells gamma delta.” – However, this is not good enough, plz explicitly mention the immune cell types whose infiltration increases in AMI samples compared to normal samples.
7. Also provide more insights what it may mean in terms of the overall prognosis for AMI patients and if this could be used as a biomarker or provide us potential insights how they could be regulating AMI/necroptosis in AMI patients. Put your results in some context as you are presenting them and not just leave it just for the discussion.
8. While the authors have discussed these NRDEGs as potential biomarkers for AMI, the sample datasets had samples from patients already suffering from AMI. Can authors add some insights on how early can these 6 hub NRDEGs be detected, and can they have any value in terms of predicting long term AMI prognosis/patient outcomes?
Minor Suggestions/Edits:
1. Article needs to be proofread and grammatical errors need correction. Couple of selected examples are below.
a. For example, Page 7, lines 52 through 56, are grammatically incorrect.
b. Page 7, line 62-63, this is an instance of sentence run-on.
Authors have explained their methods in detail and have valid designs. No concern on this part as well.
The authors have valid findings, I have no concerns in this aspect of this study
Strengths:
1. All results were validated with qPCR and an independent data set giving more credibility to the findings. The authors have explicitly highlighted this point, and this is a very important aspect when result replication is not guaranteed.
2. RT-qPCR results were for most part consistent with the findings and analysis from 2 independent data sets - GSE66360 and GSE97320.
3. The discussion section is well done in terms of insights into potential mechanisms and how their findings fit in with the current literature.
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